Gene Set Testing

The list of differentially expressed genes is sometimes so long that its interpretation becomes cumbersome and time consuming. It may also be very short while some genes have low p-value yet higher than the given threshold.

A common downstream procedure to combine information across genes is gene set testing. It aims at finding pathways or gene networks the differentially expressed genes play a role in.

Various ways exist to test for enrichment of biological pathways. We will look into over representation and gene set enrichment analyses.

A gene set comprises genes that share a biological function, chromosomal location, or any other relevant criterion.

To save time and effort there are a number of packages that make applying these tests to a large number of gene sets simpler, and which will import gene lists for testing from various sources.

Today we will use clusterProfiler.

Over-representation

This method tests whether genes in a pathway are present in a subset of our data in a higher number than expected by chance (explanations derived from the clusterProfiler manual).

Genes in the experiment are split in two ways:

  • annotated to the pathway or not
  • differentially expressed or not

We can then create a contingency table with:

  • rows: genes in pathway or not
  • columns: genes differentially expressed or not

And test for independence of the two variables with the Fisher exact test.

clusterProfiler

clusterprofiler (Yu et al. 2012) supports direct online access of the current KEGG database (KEGG: Kyoto Encyclopedia of Genes and Genomes), rather than relying on R annotation packages. It also provides some nice visualisation options.

We first search the resource for mouse data:

library(tidyverse)
library(clusterProfiler)

search_kegg_organism('mouse', by='common_name')
##      kegg_code                            scientific_name
## 26        mmur                         Microcebus murinus
## 30         mmu                               Mus musculus
## 31        mcal                                 Mus caroli
## 32        mpah                                 Mus pahari
## 34        mcoc                            Mastomys coucha
## 40        pleu                        Peromyscus leucopus
## 50        plop         Perognathus longimembris pacificus
## 113       mmyo                              Myotis myotis
## 188       csti                            Colius striatus
## 5722       asf Candidatus Arthromitus sp. SFB-mouse-Japan
## 5723       asm   Candidatus Arthromitus sp. SFB-mouse-Yit
## 5724       aso    Candidatus Arthromitus sp. SFB-mouse-NL
##                                     common_name
## 26                             gray mouse lemur
## 30                                  house mouse
## 31                                 Ryukyu mouse
## 32                                  shrew mouse
## 34                  southern multimammate mouse
## 40                           white-footed mouse
## 50                         Pacific pocket mouse
## 113                     greater mouse-eared bat
## 188                          speckled mousebird
## 5722 Candidatus Arthromitus sp. SFB-mouse-Japan
## 5723   Candidatus Arthromitus sp. SFB-mouse-Yit
## 5724    Candidatus Arthromitus sp. SFB-mouse-NL

We will use the ‘mmu’ ‘kegg_code’.

KEGG enrichment analysis

The input for the KEGG enrichment analysis is the list of gene IDs of significant genes.

We now load the R object keeping the outcome of the differential expression analysis for the d11 contrast.

res_d11_shrink <- readRDS("preprocessed/r_objects/res_infected_vs_uninfected_d11_annotated_shrink.rds")

We will only use genes that have:

  • an adjusted p-value (FDR, False Discovery Rate) of less than 0.05
  • and an absolute fold change greater than 2.

We need to remember to eliminate genes with missing values in the FDR as a result of the independent filtering by DESeq2.

For this tool we need to use Entrez IDs, so we will also need to eliminate genes with a missing Entrez ID (NA values in the ‘Entrez’ column).

sig_genes_entrez <- res_d11_shrink %>% 
  drop_na(Entrez, padj) %>% 
  filter(padj < 0.05 & abs(log2FoldChange) > 1) %>% 
  pull(Entrez)

kegg_results <- enrichKEGG(gene = sig_genes_entrez, organism = 'mmu')
## Reading KEGG annotation online: "https://rest.kegg.jp/link/mmu/pathway"...
## Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/mmu"...
as_tibble(kegg_results)
## # A tibble: 74 × 14
##    category           subcategory ID    Description GeneRatio BgRatio RichFactor
##    <chr>              <chr>       <chr> <chr>       <chr>     <chr>        <dbl>
##  1 Human Diseases     Infectious… mmu0… Herpes sim… 63/351    213/10…      0.296
##  2 Organismal Systems Immune sys… mmu0… Antigen pr… 39/351    87/105…      0.448
##  3 Human Diseases     Infectious… mmu0… Epstein-Ba… 55/351    228/10…      0.241
##  4 Human Diseases     Immune dis… mmu0… Graft-vers… 31/351    60/105…      0.517
##  5 Human Diseases     Endocrine … mmu0… Type I dia… 32/351    67/105…      0.478
##  6 Cellular Processes Transport … mmu0… Phagosome   47/351    183/10…      0.257
##  7 Human Diseases     Immune dis… mmu0… Allograft … 30/351    60/105…      0.5  
##  8 Human Diseases     Infectious… mmu0… Influenza A 45/351    174/10…      0.259
##  9 Environmental Inf… Signaling … mmu0… Cell adhes… 44/351    181/10…      0.243
## 10 Human Diseases     Infectious… mmu0… Leishmania… 28/351    70/105…      0.4  
## # ℹ 64 more rows
## # ℹ 7 more variables: FoldEnrichment <dbl>, zScore <dbl>, pvalue <dbl>,
## #   p.adjust <dbl>, qvalue <dbl>, geneID <chr>, Count <int>

Visualise a pathway in a browser

clusterProfiler has a function browseKegg to view the KEGG pathway in a browser, highlighting the genes we selected as differentially expressed.

We will show one of the top hits: pathway ‘mmu04612’ for ‘Antigen processing and presentation’.

browseKEGG(kegg_results, 'mmu04612')

Visualise a pathway as a file

The package pathview (Luo et al. 2013) can be used to generate figures of KEGG pathways.

One advantage over the clusterProfiler browser function browseKEGG() is that genes can be coloured according to fold change levels in our data. To do this we need to pass pathview a named vector of fold change values (one could in fact colour by any numeric vector, e.g. p-value).

The package plots the KEGG pathway to a png file in the working directory.

library(pathview)
lfc <- res_d11_shrink$log2FoldChange
names(lfc) <- res_d11_shrink$Entrez
pathview(gene.data = lfc, 
         pathway.id = "mmu04612", 
         species = "mmu", 
         limit = list(gene = 20, cpd = 1))

mmu04612.pathview.png:

mmu04612 - Antigen processing and presentation

Exercise 1

  1. Use pathview to export a figure for “mmu04659” or “mmu04658”, but this time only use genes that are statistically significant at padj < 0.01

Answer

First, we need to create a new list of genes using the new filter:

lfc <- res_d11_shrink %>% 
  drop_na(padj, Entrez) %>% 
  filter(padj < 0.01) %>% 
  pull(log2FoldChange, Entrez) 

Then, we can use the pathview() function as demonstrated above:

pathview(gene.data = lfc, 
         pathway.id = "mmu04659", 
         species = "mmu", 
         limit = list(gene = 5, cpd = 1))
## 'select()' returned 1:1 mapping between keys and columns
## Info: Working in directory /mnt/c/Users/hugot/Documents/crit/repos/rnaseq-course-nxf/course_files
## Info: Writing image file mmu04659.pathview.png

This outputs an image to the local working directory.

mmu04659 - Th17 cell differentiation

GO term enrichment analysis

clusterProfiler can also perform over-representation analysis on GO terms using the function enrichGO(). For this analysis we will use Ensembl gene IDs instead of Entrez IDs and in order to do this we need to load another package which contains the mouse database called org.Mm.eg.db.

To run the GO enrichment analysis, this time we also need a couple of extra things. Firstly, we should provide a list of the ‘universe’ of all the genes in our DE analysis not just the ones we have selected as significant.

Gene Ontology terms are divided into 3 categories.

  • Metabolic Functions
  • Biological Processes
  • Cellular Components

For this analysis we will narrow our search terms in the ‘Biological Processes’ Ontology so we can add the parameter “BP” with the ‘ont’ argument (the default is Molecular Functions).

library(org.Mm.eg.db)

# get ENSEMBL IDs for significant genes
sig_genes_ensembl <-  res_d11_shrink %>% 
  drop_na(padj) %>% 
  filter(padj < 0.01 & abs(log2FoldChange) > 2) %>% 
  pull(GeneID)

# get names for all the genes we have data for
universe <- res_d11_shrink$GeneID

# run enrichment analysis
go_results <- enrichGO(gene = sig_genes_ensembl, 
                       universe = universe,
                       OrgDb = org.Mm.eg.db,
                       keyType = "ENSEMBL",
                       ont = "BP",
                       pvalueCutoff = 0.01,
                       readable = TRUE)

We can use the barplot() function to visualise the results. Count is the number of differentially expressed in each gene ontology term.

barplot(go_results, showCategory = 20)

An alternative is the dotplot() version, which can be more informative. Gene ratio is Count divided by the number of genes in that GO term.

dotplot(go_results, font.size = 14)

Another visualisation that can be nice to try is the emapplot() which shows the overlap between genes in the different GO terms.

library(enrichplot)
## enrichplot v1.28.2 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
## 
## Please cite:
## 
## Guangchuang Yu, Li-Gen Wang, Yanyan Han and Qing-Yu He.
## clusterProfiler: an R package for comparing biological themes among
## gene clusters. OMICS: A Journal of Integrative Biology. 2012,
## 16(5):284-287
ego_pt <- pairwise_termsim(go_results)
emapplot(ego_pt)

GSEA analysis

Gene Set Enrichment Analysis (GSEA) identifies gene sets that are enriched in the dataset between samples (Subramanian et al. 2005).

The software is distributed by the Broad Institute and is freely available for use by academic and non-profit organisations. The Broad also provide a number of very well curated gene sets for testing against your data - the Molecular Signatures Database (MSigDB). These are collections of human genes. Fortunately, these lists have been translated to mouse equivalents by the Walter+Eliza Hall Institute Bioinformatics service and made available for download. They are now also available from a recent R package msigdbr, which we will use.

Let’s load msigdbr now.

library(msigdbr)

The analysis is performed by:

  1. ranking all genes in the data set
  2. identifying in the ranked data set the rank positions of all members of the gene set
  3. calculating an enrichment score (ES) that represents the difference between the observed rankings and that which would be expected assuming a random rank distribution.

The article describing the original software is available here, while this commentary on GSEA provides a shorter description.

We will use clusterProfiler’s GSEA package (Yu et al. 2012) that implements the same algorithm in R.

Rank genes

We need to provide GSEA with a vector containing values for a given gene mtric, e.g. log(fold change), sorted in decreasing order.

To start with we will simply use a rank the genes based on their fold change.

We must exclude genes with no Ensembl ID.

Also, we should use the shrunk LFC values.

ranked_genes <- res_d11_shrink %>%
  drop_na(GeneID, padj, log2FoldChange) %>%
  mutate(rank = log2FoldChange) %>%
  arrange(desc(rank)) %>%
  pull(rank, GeneID)

Load pathways

We will load the MSigDB Hallmark gene set with msigdbr, setting the category parameter to ‘H’ for Hallmark gene set. The object created is a tibble with information on each {gene set; gene} pair (one per row). We will only keep the the gene set name, gene Ensembl ID.

term2gene <- msigdbr(species = "Mus musculus", category = "H") %>% 
  dplyr::select(gs_name, ensembl_gene)
## Using human MSigDB with ortholog mapping to mouse. Use `db_species = "MM"` for mouse-native gene sets.
## This message is displayed once per session.
## Warning: The `category` argument of `msigdbr()` is deprecated as of msigdbr 10.0.0.
## ℹ Please use the `collection` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
term2name <- msigdbr(species = "Mus musculus", category = "H") %>% 
  dplyr::select(gs_name, gs_description) %>% 
  distinct()

Conduct analysis

Arguments passed to GSEA include:

  • ranked genes
  • pathways
  • gene set minimum size
  • gene set maximum size
gsea_results <- GSEA(ranked_genes,
                     TERM2GENE = term2gene,
                     TERM2NAME = term2name,
                     pvalueCutoff = 1.00, 
                     minGSSize = 15,
                     maxGSSize = 500)
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...

Let’s look at the top 10 results, based on the normalised enrichment score (NES):

gsea_results %>% 
  # convert to regular data.frame/tibble
  as_tibble() %>% 
  # get the top genes based on adjusted p-value
  top_n(10, wt = -p.adjust) %>%
  # sort by normalised enrichment score
  arrange(desc(abs(NES)))  

Enrichment score plot

The enrichment score plot displays along the x-axis that represents the decreasing gene rank:

  • genes involved in the pathway under scrutiny: one black tick per gene in the pathway (no tick for genes not in the pathway)
  • the enrichment score: the green curve shows the difference between the observed rankings and that which would be expected assuming a random rank distribution.
gseaplot(gsea_results, 
         geneSetID = "HALLMARK_INFLAMMATORY_RESPONSE", 
         title = "HALLMARK_INFLAMMATORY_RESPONSE")

Remember to check the GSEA article for the complete explanation.

Exercise 2

Another common way to rank the genes is to order by p-value while sorting, so that upregulated genes are at the start and downregulated at the end. You can do this combining the sign of the fold change and the p-value.

  1. Rank the genes by statistical significance - you will need to create a new ranking value using -log10(pvalue) * sign(log2FoldChange).

  2. Run GSEA using the new ranked genes and the H pathways.

  3. Conduct the same analysis for the day 33 Infected vs Uninfected contrast. Make sure to first load the results using the following:

    # read d33 data in
    res_d33_shrink <- readRDS("preprocessed/r_objects/res_infected_vs_uninfected_d33_annotated_shrink.rds")

Answer

First load the pathway details if you have not already done so.

library(msigdbr)
term2gene <- msigdbr(species = "Mus musculus", category = "H") %>% 
  dplyr::select(gs_name, ensembl_gene)
term2name <- msigdbr(species = "Mus musculus", category = "H") %>% 
  dplyr::select(gs_name, gs_description) %>% 
  distinct()

1.

We can generate a new table of ranked genes using the new ranking based on p-values:

# rank genes
ranked_genes_pval <- res_d11_shrink %>%
  drop_na(GeneID, pvalue, log2FoldChange) %>%
  mutate(rank = -log10(pvalue) * sign(log2FoldChange)) %>%
  arrange(desc(rank)) %>%
  pull(rank, GeneID)

The result is a named vector with sorted p-values. Remember that a high -log10(pvalue) corresponds to a very low (i.e. significant) p-value. By multiplying -log10(pvalue) by the sign of the log2FoldChange we ensure that our p-values are ordered from upregulated to downregulated genes. Here is a peek at the first (head()) and last (tail()) few genes of our ranked gene vector:

head(ranked_genes_pval)
## ENSMUSG00000046879 ENSMUSG00000078853 ENSMUSG00000069874 ENSMUSG00000060586 
##          119.08761          111.37314          110.87312          110.33734 
## ENSMUSG00000024610 ENSMUSG00000054072 
##           96.34973           95.07151
tail(ranked_genes_pval)
## ENSMUSG00000066443 ENSMUSG00000040488 ENSMUSG00000004891 ENSMUSG00000021342 
##          -7.433218          -7.710282          -7.728996          -8.147132 
## ENSMUSG00000066842 ENSMUSG00000020154 
##          -8.628705         -11.697586

2.

Once we have our ranked genes, we can use the GSEA() function as demonstrated earlier:

# conduct analysis:
gsea_res_pval <- GSEA(ranked_genes_pval,
                      TERM2GENE = term2gene,
                      TERM2NAME = term2name,
                      pvalueCutoff = 1.00, 
                      minGSSize = 15,
                      maxGSSize = 500)
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some of the pathways the P-values were likely overestimated. For
## such pathways log2err is set to NA.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some pathways, in reality P-values are less than 1e-10. You can
## set the `eps` argument to zero for better estimation.
## leading edge analysis...
## done...

To view the results we can convert this object to a regular data.frame/tibble, extract the top results based on p-value and sort them by the normalised enrichment score:

gsea_res_pval %>% 
  as_tibble() %>% 
  top_n(10, wt=-p.adjust) %>%
  arrange(desc(abs(NES)))

3.

We repeat the analysis we just did for d11, but using the d33 contrast instead:

# rank genes
ranked_genes_d33 <- res_d33_shrink %>%
  drop_na(GeneID, pvalue, log2FoldChange) %>%
  mutate(rank = -log10(pvalue) * sign(log2FoldChange)) %>%
  arrange(desc(rank)) %>%
  pull(rank, GeneID)

# perform analysis
gsea_res_d33 <- GSEA(ranked_genes_d33,
                    TERM2GENE = term2gene,
                    TERM2NAME = term2name,
                    pvalueCutoff = 1.00, 
                    minGSSize = 15,
                    maxGSSize = 500)
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 3 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some of the pathways the P-values were likely overestimated. For
## such pathways log2err is set to NA.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some pathways, in reality P-values are less than 1e-10. You can
## set the `eps` argument to zero for better estimation.
## leading edge analysis...
## done...

View the results:

as_tibble(gsea_res_d33) %>% 
  top_n(10, wt = -p.adjust) %>%
  arrange(desc(abs(NES)))

References

Luo, Weijun, Brouwer, and Cory. 2013. “Pathview: An R/Bioconductor Package for Pathway-Based Data Integration and Visualization.” Bioinformatics 29 (14): 1830–1. https://doi.org/10.1093/bioinformatics/btt285.

Subramanian, Aravind, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences 102 (43): 15545–50. https://doi.org/10.1073/pnas.0506580102.

Yu, Guangchuang, Li-Gen Wang, Yanyan Han, and Qing-Yu He. 2012. “ClusterProfiler: An R Package for Comparing Biological Themes Among Gene Clusters.” OMICS: A Journal of Integrative Biology 16 (5): 284–87. https://doi.org/10.1089/omi.2011.0118.